PS 31-121 - Soil microbial community responses to prairie restoration land management practices

Background/Question/Methods

Critical ecosystem functions such as decomposition and nutrient cycling are driven by microbial communities within soil. As such, it is important to examine the effect of restoration practices, such as the presence of native grazers and prescribed burning, on these microbes and the soil they inhabit. The Nachusa Grasslands provides a chronosequence of restored tallgrass prairies ranging in restoration age from 5 to 33 years, as well as remnant prairies, and agricultural fields. We have sampled and sequenced soil microbial communities at these locations for the last 8 years to assess how common restoration practices affect microbial communities. Geochemical analysis was performed to quantify soil carbon and nitrogen content and stable isotopes, pH, and moisture. These data will be compared to microbial community compositions between and within plots to assess the impact of burn regime, bison introduction, season/year/age, and soil geochemistry.

Results/Conclusions

Results indicate a relatively rapid convergence of microbial communities in restoration sites with those of remnant prairies, suggesting that these communities may be valid indicators of restoration success. Prescribed burning accelerates soil C:N increases occurring with age, and while we see a decrease in relative abundance of groups like Nitrospira due to burning, we also see an increase in abundance of groups like Acidobacteria. Both groups are capable of nitrification and are adapted to oligotrophic conditions. This prompts the question of whether the functional diversity of the community is being maintained even while taxonomic diversity may be changing. It is likely that the success of many plant and small animal communities depend on nutrient cycling provided by these microbes, highlighting the critical interdependence of all members of these complex ecosystems. We aim to discover if differences in microbial community structure and function in grasslands are driven by restoration management practices, including burning and grazing, and to use these data to forecast prairie restoration success via novel machine learning techniques.